



import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

a      :  Optional[Any]			     =			16
a      :  Optional[Any]			     =			32







def  lowerCAmelCase_	(lowerCAmelCase__:		Accelerator		,						lowerCAmelCase__:		int = 1_6  ):





			"""simple docstring"""


			UpperCAmelCase_:		Tuple							      =     AutoTokenizer.from_pretrained("""bert-base-cased"""  )
			UpperCAmelCase_:		str							      =     load_dataset("""glue"""		,						"""mrpc"""  )

			def tokenize_function(lowerCAmelCase__:		Any  ):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_:		Tuple							      =     tokenizer(examples["""sentence1"""]		,						examples["""sentence2"""]		,						truncation=lowerCAmelCase__		,						max_length=lowerCAmelCase__  )
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			# starting with the main process first:
			with accelerator.main_process_first():
						UpperCAmelCase_:		Any							      =     datasets.map(
						    lowerCAmelCase__		,						batched=lowerCAmelCase__		,						remove_columns=["""idx""", """sentence1""", """sentence2"""]		,						)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			UpperCAmelCase_:		Union[str, Any]							      =     tokenized_datasets.rename_column("""label"""		,						"""labels"""  )

			def collate_fn(lowerCAmelCase__:		List[Any]  ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						UpperCAmelCase_:		Tuple							      =     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									UpperCAmelCase_:		Optional[int]							      =     1_6
						elif accelerator.mixed_precision != "no":
									UpperCAmelCase_:		List[str]							      =     8
						else:
									UpperCAmelCase_:		Any							      =     None

						return tokenizer.pad(
						    lowerCAmelCase__		,						padding="""longest"""		,						max_length=lowerCAmelCase__		,						pad_to_multiple_of=lowerCAmelCase__		,						return_tensors="""pt"""		,						)

			# Instantiate dataloaders.
			UpperCAmelCase_:		int							      =     DataLoader(
			    tokenized_datasets["""train"""]		,						shuffle=lowerCAmelCase__		,						collate_fn=lowerCAmelCase__		,						batch_size=lowerCAmelCase__  )
			UpperCAmelCase_:		Union[str, Any]							      =     DataLoader(
			    tokenized_datasets["""validation"""]		,						shuffle=lowerCAmelCase__		,						collate_fn=lowerCAmelCase__		,						batch_size=lowerCAmelCase__  )

			return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	a      :  List[str]			     =			mocked_dataloaders  # noqa: F811







def  lowerCAmelCase_	(lowerCAmelCase__:		List[Any]		,						lowerCAmelCase__:		Optional[int]  ):





			"""simple docstring"""


			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,						lowerCAmelCase__  ) == "1":
						UpperCAmelCase_:		Optional[Any]							      =     2
			# Initialize Accelerator

			# New Code #
			# We pass in "all" to `log_with` to grab all available trackers in the environment
			# Note: If using a custom `Tracker` class, should be passed in here such as:
			# >>> log_with = ["all", MyCustomTrackerClassInstance()]
			if args.with_tracking:
						UpperCAmelCase_:		Optional[int]							      =     Accelerator(
						    cpu=args.cpu		,						mixed_precision=args.mixed_precision		,						log_with="""all"""		,						project_dir=args.project_dir  )
			else:
						UpperCAmelCase_:		Tuple							      =     Accelerator(cpu=args.cpu		,						mixed_precision=args.mixed_precision  )
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase_:		Union[str, Any]							      =     config["""lr"""]
			UpperCAmelCase_:		Tuple							      =     int(config["""num_epochs"""]  )
			UpperCAmelCase_:		Optional[int]							      =     int(config["""seed"""]  )
			UpperCAmelCase_:		Any							      =     int(config["""batch_size"""]  )
			set_seed(lowerCAmelCase__  )

			UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     get_dataloaders(lowerCAmelCase__		,						lowerCAmelCase__  )
			UpperCAmelCase_:		Dict							      =     evaluate.load("""glue"""		,						"""mrpc"""  )

			# If the batch size is too big we use gradient accumulation
			UpperCAmelCase_:		int							      =     1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						UpperCAmelCase_:		Dict							      =     batch_size // MAX_GPU_BATCH_SIZE
						UpperCAmelCase_:		Tuple							      =     MAX_GPU_BATCH_SIZE

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			UpperCAmelCase_:		Optional[int]							      =     AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""		,						return_dict=lowerCAmelCase__  )

			# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
			# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
			# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
			UpperCAmelCase_:		Tuple							      =     model.to(accelerator.device  )

			# Instantiate optimizer
			UpperCAmelCase_:		int							      =     AdamW(params=model.parameters()		,						lr=lowerCAmelCase__  )

			# Instantiate scheduler
			UpperCAmelCase_:		List[str]							      =     get_linear_schedule_with_warmup(
			    optimizer=lowerCAmelCase__		,						num_warmup_steps=1_0_0		,						num_training_steps=(len(lowerCAmelCase__  ) * num_epochs) // gradient_accumulation_steps		,						)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_:		List[Any]							      =     accelerator.prepare(
			    lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )

			# New Code #
			# We need to initialize the trackers we use. Overall configurations can also be stored
			if args.with_tracking:
						UpperCAmelCase_:		Optional[Any]							      =     os.path.split(lowerCAmelCase__  )[-1].split("""."""  )[0]
						accelerator.init_trackers(lowerCAmelCase__		,						lowerCAmelCase__  )

			# Now we train the model
			for epoch in range(lowerCAmelCase__  ):
						model.train()
						# New Code #
						# For our tracking example, we will log the total loss of each epoch
						if args.with_tracking:
									UpperCAmelCase_:		Tuple							      =     0
						for step, batch in enumerate(lowerCAmelCase__  ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device  )
									UpperCAmelCase_:		Optional[Any]							      =     model(**lowerCAmelCase__  )
									UpperCAmelCase_:		Any							      =     outputs.loss
									# New Code #
									if args.with_tracking:
												total_loss += loss.detach().float()
									UpperCAmelCase_:		str							      =     loss / gradient_accumulation_steps
									accelerator.backward(lowerCAmelCase__  )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						for step, batch in enumerate(lowerCAmelCase__  ):
									# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
									batch.to(accelerator.device  )
									with torch.no_grad():
												UpperCAmelCase_:		Union[str, Any]							      =     model(**lowerCAmelCase__  )
									UpperCAmelCase_:		Union[str, Any]							      =     outputs.logits.argmax(dim=-1  )
									UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     accelerator.gather_for_metrics((predictions, batch["""labels"""])  )
									metric.add_batch(
									    predictions=lowerCAmelCase__		,						references=lowerCAmelCase__		,						)

						UpperCAmelCase_:		Optional[Any]							      =     metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'epoch {epoch}:'		,						lowerCAmelCase__  )

						# New Code #
						# To actually log, we call `Accelerator.log`
						# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
						if args.with_tracking:
									accelerator.log(
									    {
									        """accuracy""": eval_metric["""accuracy"""],
									        """f1""": eval_metric["""f1"""],
									        """train_loss""": total_loss.item() / len(lowerCAmelCase__  ),
									        """epoch""": epoch,
									    }		,						step=lowerCAmelCase__		,						)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
			if args.with_tracking:
						accelerator.end_training()







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Union[str, Any]							      =     argparse.ArgumentParser(description="""Simple example of training script."""  )
			parser.add_argument(
			    """--mixed_precision"""		,						type=lowerCAmelCase__		,						default=lowerCAmelCase__		,						choices=["""no""", """fp16""", """bf16""", """fp8"""]		,						help="""Whether to use mixed precision. Choose"""
			    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
			    """and an Nvidia Ampere GPU."""		,						)
			parser.add_argument("""--cpu"""		,						action="""store_true"""		,						help="""If passed, will train on the CPU."""  )
			parser.add_argument(
			    """--with_tracking"""		,						action="""store_true"""		,						help="""Whether to load in all available experiment trackers from the environment and use them for logging."""		,						)
			parser.add_argument(
			    """--project_dir"""		,						type=lowerCAmelCase__		,						default="""logs"""		,						help="""Location on where to store experiment tracking logs` and relevent project information"""		,						)
			UpperCAmelCase_:		List[Any]							      =     parser.parse_args()
			UpperCAmelCase_:		int							      =     {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
			training_function(lowerCAmelCase__		,						lowerCAmelCase__  )


if __name__ == "__main__":
	main()











from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


a      :  Dict			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''pixel_values''']

			def __init__(self,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = 1 / 255,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   None:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     size if size is not None else {"""height""": 256, """width""": 256}
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						UpperCAmelCase_:		Dict							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		int							      =     do_resize
						UpperCAmelCase_:		Any							      =     size
						UpperCAmelCase_:		List[Any]							      =     resample
						UpperCAmelCase_:		int							      =     do_center_crop
						UpperCAmelCase_:		Dict							      =     crop_size
						UpperCAmelCase_:		Tuple							      =     do_rescale
						UpperCAmelCase_:		Tuple							      =     rescale_factor
						UpperCAmelCase_:		Dict							      =     do_normalize
						UpperCAmelCase_:		Dict							      =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						UpperCAmelCase_:		int							      =     image_std if image_std is not None else IMAGENET_STANDARD_STD

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Tuple							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return resize(
						    SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   resample=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return center_crop(SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   int:
						return rescale(SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						return normalize(SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,   **SCREAMING_SNAKE_CASE_,   )       ->   PIL.Image.Image:
						UpperCAmelCase_:		Any							      =     do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase_:		Dict							      =     resample if resample is not None else self.resample
						UpperCAmelCase_:		Tuple							      =     do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCAmelCase_:		str							      =     do_rescale if do_rescale is not None else self.do_rescale
						UpperCAmelCase_:		List[Any]							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCAmelCase_:		List[Any]							      =     do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase_:		Optional[Any]							      =     image_mean if image_mean is not None else self.image_mean
						UpperCAmelCase_:		Dict							      =     image_std if image_std is not None else self.image_std

						UpperCAmelCase_:		Any							      =     size if size is not None else self.size
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     crop_size if crop_size is not None else self.crop_size
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		str							      =     make_list_of_images(SCREAMING_SNAKE_CASE_					)

						if not valid_images(SCREAMING_SNAKE_CASE_					):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

						if do_resize and size is None or resample is None:
									raise ValueError("""Size and resample must be specified if do_resize is True."""					)

						if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

						if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

						# All transformations expect numpy arrays.
						UpperCAmelCase_:		Tuple							      =     [to_numpy_array(SCREAMING_SNAKE_CASE_					) for image in images]

						if do_resize:
									UpperCAmelCase_:		int							      =     [self.resize(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_center_crop:
									UpperCAmelCase_:		Dict							      =     [self.center_crop(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_rescale:
									UpperCAmelCase_:		Optional[int]							      =     [self.rescale(image=SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_normalize:
									UpperCAmelCase_:		int							      =     [self.normalize(image=SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_,   tensor_type=SCREAMING_SNAKE_CASE_					)






